{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RealTimePanoptic.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yb5kUu07dEo6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "3edf89d8-c96f-4dc6-9ef4-076ccf8c63eb"
      },
      "source": [
        "%%shell\n",
        "git clone https://github.com/pushkar-khetrapal/realtime_panoptic.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'realtime_panoptic'...\n",
            "remote: Enumerating objects: 66, done.\u001b[K\n",
            "remote: Counting objects:   1% (1/66)\u001b[K\rremote: Counting objects:   3% (2/66)\u001b[K\rremote: Counting objects:   4% (3/66)\u001b[K\rremote: Counting objects:   6% (4/66)\u001b[K\rremote: Counting objects:   7% (5/66)\u001b[K\rremote: Counting objects:   9% (6/66)\u001b[K\rremote: Counting objects:  10% (7/66)\u001b[K\rremote: Counting objects:  12% (8/66)\u001b[K\rremote: Counting objects:  13% (9/66)\u001b[K\rremote: Counting objects:  15% (10/66)\u001b[K\rremote: Counting objects:  16% (11/66)\u001b[K\rremote: Counting objects:  18% (12/66)\u001b[K\rremote: Counting objects:  19% (13/66)\u001b[K\rremote: Counting objects:  21% (14/66)\u001b[K\rremote: Counting objects:  22% (15/66)\u001b[K\rremote: Counting objects:  24% (16/66)\u001b[K\rremote: Counting objects:  25% (17/66)\u001b[K\rremote: Counting objects:  27% (18/66)\u001b[K\rremote: Counting objects:  28% (19/66)\u001b[K\rremote: Counting objects:  30% (20/66)\u001b[K\rremote: Counting objects:  31% (21/66)\u001b[K\rremote: Counting objects:  33% (22/66)\u001b[K\rremote: Counting objects:  34% (23/66)\u001b[K\rremote: Counting objects:  36% (24/66)\u001b[K\rremote: Counting objects:  37% (25/66)\u001b[K\rremote: Counting objects:  39% (26/66)\u001b[K\rremote: Counting objects:  40% (27/66)\u001b[K\rremote: Counting objects:  42% (28/66)\u001b[K\rremote: Counting objects:  43% (29/66)\u001b[K\rremote: Counting objects:  45% (30/66)\u001b[K\rremote: Counting objects:  46% (31/66)\u001b[K\rremote: Counting objects:  48% (32/66)\u001b[K\rremote: Counting objects:  50% (33/66)\u001b[K\rremote: Counting objects:  51% (34/66)\u001b[K\rremote: Counting objects:  53% (35/66)\u001b[K\rremote: Counting objects:  54% (36/66)\u001b[K\rremote: Counting objects:  56% (37/66)\u001b[K\rremote: Counting objects:  57% (38/66)\u001b[K\rremote: Counting objects:  59% (39/66)\u001b[K\rremote: Counting objects:  60% (40/66)\u001b[K\rremote: Counting objects:  62% (41/66)\u001b[K\rremote: Counting objects:  63% (42/66)\u001b[K\rremote: Counting objects:  65% (43/66)\u001b[K\rremote: Counting objects:  66% (44/66)\u001b[K\rremote: Counting objects:  68% (45/66)\u001b[K\rremote: Counting objects:  69% (46/66)\u001b[K\rremote: Counting objects:  71% (47/66)\u001b[K\rremote: Counting objects:  72% (48/66)\u001b[K\rremote: Counting objects:  74% (49/66)\u001b[K\rremote: Counting objects:  75% (50/66)\u001b[K\rremote: Counting objects:  77% (51/66)\u001b[K\rremote: Counting objects:  78% (52/66)\u001b[K\rremote: Counting objects:  80% (53/66)\u001b[K\rremote: Counting objects:  81% (54/66)\u001b[K\rremote: Counting objects:  83% (55/66)\u001b[K\rremote: Counting objects:  84% (56/66)\u001b[K\rremote: Counting objects:  86% (57/66)\u001b[K\rremote: Counting objects:  87% (58/66)\u001b[K\rremote: Counting objects:  89% (59/66)\u001b[K\rremote: Counting objects:  90% (60/66)\u001b[K\rremote: Counting objects:  92% (61/66)\u001b[K\rremote: Counting objects:  93% (62/66)\u001b[K\rremote: Counting objects:  95% (63/66)\u001b[K\rremote: Counting objects:  96% (64/66)\u001b[K\rremote: Counting objects:  98% (65/66)\u001b[K\rremote: Counting objects: 100% (66/66)\u001b[K\rremote: Counting objects: 100% (66/66), done.\u001b[K\n",
            "remote: Compressing objects:   2% (1/47)\u001b[K\rremote: Compressing objects:   4% (2/47)\u001b[K\rremote: Compressing objects:   6% (3/47)\u001b[K\rremote: Compressing objects:   8% (4/47)\u001b[K\rremote: Compressing objects:  10% (5/47)\u001b[K\rremote: Compressing objects:  12% (6/47)\u001b[K\rremote: Compressing objects:  14% (7/47)\u001b[K\rremote: Compressing objects:  17% (8/47)\u001b[K\rremote: Compressing objects:  19% (9/47)\u001b[K\rremote: Compressing objects:  21% (10/47)\u001b[K\rremote: Compressing objects:  23% (11/47)\u001b[K\rremote: Compressing objects:  25% (12/47)\u001b[K\rremote: Compressing objects:  27% (13/47)\u001b[K\rremote: Compressing objects:  29% (14/47)\u001b[K\rremote: Compressing objects:  31% (15/47)\u001b[K\rremote: Compressing objects:  34% (16/47)\u001b[K\rremote: Compressing objects:  36% (17/47)\u001b[K\rremote: Compressing objects:  38% (18/47)\u001b[K\rremote: Compressing objects:  40% (19/47)\u001b[K\rremote: Compressing objects:  42% (20/47)\u001b[K\rremote: Compressing objects:  44% (21/47)\u001b[K\rremote: Compressing objects:  46% (22/47)\u001b[K\rremote: Compressing objects:  48% (23/47)\u001b[K\rremote: Compressing objects:  51% (24/47)\u001b[K\rremote: Compressing objects:  53% (25/47)\u001b[K\rremote: Compressing objects:  55% (26/47)\u001b[K\rremote: Compressing objects:  57% (27/47)\u001b[K\rremote: Compressing objects:  59% (28/47)\u001b[K\rremote: Compressing objects:  61% (29/47)\u001b[K\rremote: Compressing objects:  63% (30/47)\u001b[K\rremote: Compressing objects:  65% (31/47)\u001b[K\rremote: Compressing objects:  68% (32/47)\u001b[K\rremote: Compressing objects:  70% (33/47)\u001b[K\rremote: Compressing objects:  72% (34/47)\u001b[K\rremote: Compressing objects:  74% (35/47)\u001b[K\rremote: Compressing objects:  76% (36/47)\u001b[K\rremote: Compressing objects:  78% (37/47)\u001b[K\rremote: Compressing objects:  80% (38/47)\u001b[K\rremote: Compressing objects:  82% (39/47)\u001b[K\rremote: Compressing objects:  85% (40/47)\u001b[K\rremote: Compressing objects:  87% (41/47)\u001b[K\rremote: Compressing objects:  89% (42/47)\u001b[K\rremote: Compressing objects:  91% (43/47)\u001b[K\rremote: Compressing objects:  93% (44/47)\u001b[K\rremote: Compressing objects:  95% (45/47)\u001b[K\rremote: Compressing objects:  97% (46/47)\u001b[K\rremote: Compressing objects: 100% (47/47)\u001b[K\rremote: Compressing objects: 100% (47/47), done.\u001b[K\n",
            "Unpacking objects:   1% (1/66)   \rUnpacking objects:   3% (2/66)   \rUnpacking objects:   4% (3/66)   \rUnpacking objects:   6% (4/66)   \rUnpacking objects:   7% (5/66)   \rUnpacking objects:   9% (6/66)   \rUnpacking objects:  10% (7/66)   \rUnpacking objects:  12% (8/66)   \rUnpacking objects:  13% (9/66)   \rUnpacking objects:  15% (10/66)   \rUnpacking objects:  16% (11/66)   \rUnpacking objects:  18% (12/66)   \rUnpacking objects:  19% (13/66)   \rUnpacking objects:  21% (14/66)   \rUnpacking objects:  22% (15/66)   \rUnpacking objects:  24% (16/66)   \rUnpacking objects:  25% (17/66)   \rUnpacking objects:  27% (18/66)   \rUnpacking objects:  28% (19/66)   \rUnpacking objects:  30% (20/66)   \rUnpacking objects:  31% (21/66)   \rUnpacking objects:  33% (22/66)   \rUnpacking objects:  34% (23/66)   \rremote: Total 66 (delta 17), reused 49 (delta 10), pack-reused 0\u001b[K\n",
            "Unpacking objects:  36% (24/66)   \rUnpacking objects:  37% (25/66)   \rUnpacking objects:  39% (26/66)   \rUnpacking objects:  40% (27/66)   \rUnpacking objects:  42% (28/66)   \rUnpacking objects:  43% (29/66)   \rUnpacking objects:  45% (30/66)   \rUnpacking objects:  46% (31/66)   \rUnpacking objects:  48% (32/66)   \rUnpacking objects:  50% (33/66)   \rUnpacking objects:  51% (34/66)   \rUnpacking objects:  53% (35/66)   \rUnpacking objects:  54% (36/66)   \rUnpacking objects:  56% (37/66)   \rUnpacking objects:  57% (38/66)   \rUnpacking objects:  59% (39/66)   \rUnpacking objects:  60% (40/66)   \rUnpacking objects:  62% (41/66)   \rUnpacking objects:  63% (42/66)   \rUnpacking objects:  65% (43/66)   \rUnpacking objects:  66% (44/66)   \rUnpacking objects:  68% (45/66)   \rUnpacking objects:  69% (46/66)   \rUnpacking objects:  71% (47/66)   \rUnpacking objects:  72% (48/66)   \rUnpacking objects:  74% (49/66)   \rUnpacking objects:  75% (50/66)   \rUnpacking objects:  77% (51/66)   \rUnpacking objects:  78% (52/66)   \rUnpacking objects:  80% (53/66)   \rUnpacking objects:  81% (54/66)   \rUnpacking objects:  83% (55/66)   \rUnpacking objects:  84% (56/66)   \rUnpacking objects:  86% (57/66)   \rUnpacking objects:  87% (58/66)   \rUnpacking objects:  89% (59/66)   \rUnpacking objects:  90% (60/66)   \rUnpacking objects:  92% (61/66)   \rUnpacking objects:  93% (62/66)   \rUnpacking objects:  95% (63/66)   \rUnpacking objects:  96% (64/66)   \rUnpacking objects:  98% (65/66)   \rUnpacking objects: 100% (66/66)   \rUnpacking objects: 100% (66/66), done.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1bVB8REudi_d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "ad552cd3-14d1-4ecd-e19d-bfa034bc347a"
      },
      "source": [
        "!wget https://wallup.net/wp-content/uploads/2016/01/256862-car-landscape-vehicle-road.jpg"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-17 11:23:27--  https://wallup.net/wp-content/uploads/2016/01/256862-car-landscape-vehicle-road.jpg\n",
            "Resolving wallup.net (wallup.net)... 104.24.117.237, 172.67.163.163, 104.24.116.237, ...\n",
            "Connecting to wallup.net (wallup.net)|104.24.117.237|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [image/jpg]\n",
            "Saving to: ‘256862-car-landscape-vehicle-road.jpg’\n",
            "\n",
            "256862-car-landscap     [    <=>             ]   1.60M  1.71MB/s    in 0.9s    \n",
            "\n",
            "2020-08-17 11:23:35 (1.71 MB/s) - ‘256862-car-landscape-vehicle-road.jpg’ saved [1675401]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-lH6sOVgz0h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "194a299e-7eb3-4cf9-fc9c-a1dd8cfd1ace"
      },
      "source": [
        "!wget https://tri-ml-public.s3.amazonaws.com/github/realtime_panoptic/models/cvpr_realtime_pano_cityscapes_standalone_no_prefix.pth"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-17 11:23:39--  https://tri-ml-public.s3.amazonaws.com/github/realtime_panoptic/models/cvpr_realtime_pano_cityscapes_standalone_no_prefix.pth\n",
            "Resolving tri-ml-public.s3.amazonaws.com (tri-ml-public.s3.amazonaws.com)... 52.216.107.228\n",
            "Connecting to tri-ml-public.s3.amazonaws.com (tri-ml-public.s3.amazonaws.com)|52.216.107.228|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 122974869 (117M) [binary/octet-stream]\n",
            "Saving to: ‘cvpr_realtime_pano_cityscapes_standalone_no_prefix.pth’\n",
            "\n",
            "cvpr_realtime_pano_ 100%[===================>] 117.28M  29.3MB/s    in 4.0s    \n",
            "\n",
            "2020-08-17 11:23:43 (29.3 MB/s) - ‘cvpr_realtime_pano_cityscapes_standalone_no_prefix.pth’ saved [122974869/122974869]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-faDq0Tiirs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "967abc70-a341-43b7-ca4e-93a53176ca7d"
      },
      "source": [
        "%%bash \n",
        "pip uninstall -y apex\n",
        "git clone https://www.github.com/nvidia/apex\n",
        "cd apex\n",
        "python setup.py install"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "torch.__version__  = 1.6.0+cu101\n",
            "\n",
            "\n",
            "running install\n",
            "running bdist_egg\n",
            "running egg_info\n",
            "creating apex.egg-info\n",
            "writing apex.egg-info/PKG-INFO\n",
            "writing dependency_links to apex.egg-info/dependency_links.txt\n",
            "writing top-level names to apex.egg-info/top_level.txt\n",
            "writing manifest file 'apex.egg-info/SOURCES.txt'\n",
            "writing manifest file 'apex.egg-info/SOURCES.txt'\n",
            "installing library code to build/bdist.linux-x86_64/egg\n",
            "running install_lib\n",
            "running build_py\n",
            "creating build\n",
            "creating build/lib\n",
            "creating build/lib/apex\n",
            "copying apex/__init__.py -> build/lib/apex\n",
            "creating build/lib/apex/optimizers\n",
            "copying apex/optimizers/__init__.py -> build/lib/apex/optimizers\n",
            "copying apex/optimizers/fused_novograd.py -> build/lib/apex/optimizers\n",
            "copying apex/optimizers/fused_adagrad.py -> build/lib/apex/optimizers\n",
            "copying apex/optimizers/fused_sgd.py -> build/lib/apex/optimizers\n",
            "copying apex/optimizers/fused_adam.py -> build/lib/apex/optimizers\n",
            "copying apex/optimizers/fused_lamb.py -> build/lib/apex/optimizers\n",
            "creating build/lib/apex/parallel\n",
            "copying apex/parallel/multiproc.py -> build/lib/apex/parallel\n",
            "copying apex/parallel/__init__.py -> build/lib/apex/parallel\n",
            "copying apex/parallel/LARC.py -> build/lib/apex/parallel\n",
            "copying apex/parallel/sync_batchnorm_kernel.py -> build/lib/apex/parallel\n",
            "copying apex/parallel/optimized_sync_batchnorm_kernel.py -> build/lib/apex/parallel\n",
            "copying apex/parallel/sync_batchnorm.py -> build/lib/apex/parallel\n",
            "copying apex/parallel/distributed.py -> build/lib/apex/parallel\n",
            "copying apex/parallel/optimized_sync_batchnorm.py -> build/lib/apex/parallel\n",
            "creating build/lib/apex/reparameterization\n",
            "copying apex/reparameterization/reparameterization.py -> build/lib/apex/reparameterization\n",
            "copying apex/reparameterization/__init__.py -> build/lib/apex/reparameterization\n",
            "copying apex/reparameterization/weight_norm.py -> build/lib/apex/reparameterization\n",
            "creating build/lib/apex/multi_tensor_apply\n",
            "copying apex/multi_tensor_apply/__init__.py -> build/lib/apex/multi_tensor_apply\n",
            "copying apex/multi_tensor_apply/multi_tensor_apply.py -> build/lib/apex/multi_tensor_apply\n",
            "creating build/lib/apex/contrib\n",
            "copying apex/contrib/__init__.py -> build/lib/apex/contrib\n",
            "creating build/lib/apex/mlp\n",
            "copying apex/mlp/__init__.py -> build/lib/apex/mlp\n",
            "copying apex/mlp/mlp.py -> build/lib/apex/mlp\n",
            "creating build/lib/apex/fp16_utils\n",
            "copying apex/fp16_utils/__init__.py -> build/lib/apex/fp16_utils\n",
            "copying apex/fp16_utils/fp16util.py -> build/lib/apex/fp16_utils\n",
            "copying apex/fp16_utils/fp16_optimizer.py -> build/lib/apex/fp16_utils\n",
            "copying apex/fp16_utils/loss_scaler.py -> build/lib/apex/fp16_utils\n",
            "creating build/lib/apex/amp\n",
            "copying apex/amp/__init__.py -> build/lib/apex/amp\n",
            "copying apex/amp/amp.py -> build/lib/apex/amp\n",
            "copying apex/amp/__version__.py -> build/lib/apex/amp\n",
            "copying apex/amp/utils.py -> build/lib/apex/amp\n",
            "copying apex/amp/opt.py -> build/lib/apex/amp\n",
            "copying apex/amp/_amp_state.py -> build/lib/apex/amp\n",
            "copying apex/amp/compat.py -> build/lib/apex/amp\n",
            "copying apex/amp/frontend.py -> build/lib/apex/amp\n",
            "copying apex/amp/_process_optimizer.py -> build/lib/apex/amp\n",
            "copying apex/amp/rnn_compat.py -> build/lib/apex/amp\n",
            "copying apex/amp/handle.py -> build/lib/apex/amp\n",
            "copying apex/amp/scaler.py -> build/lib/apex/amp\n",
            "copying apex/amp/wrap.py -> build/lib/apex/amp\n",
            "copying apex/amp/_initialize.py -> build/lib/apex/amp\n",
            "creating build/lib/apex/pyprof\n",
            "copying apex/pyprof/__init__.py -> build/lib/apex/pyprof\n",
            "creating build/lib/apex/RNN\n",
            "copying apex/RNN/models.py -> build/lib/apex/RNN\n",
            "copying apex/RNN/__init__.py -> build/lib/apex/RNN\n",
            "copying apex/RNN/RNNBackend.py -> build/lib/apex/RNN\n",
            "copying apex/RNN/cells.py -> build/lib/apex/RNN\n",
            "creating build/lib/apex/normalization\n",
            "copying apex/normalization/__init__.py -> build/lib/apex/normalization\n",
            "copying apex/normalization/fused_layer_norm.py -> build/lib/apex/normalization\n",
            "creating build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/__init__.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/distributed_fused_adam_v3.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/fused_sgd.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/distributed_fused_lamb.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/fused_adam.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/fp16_optimizer.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/distributed_fused_adam.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/distributed_fused_adam_v2.py -> build/lib/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/fused_lamb.py -> build/lib/apex/contrib/optimizers\n",
            "creating build/lib/apex/contrib/groupbn\n",
            "copying apex/contrib/groupbn/__init__.py -> build/lib/apex/contrib/groupbn\n",
            "copying apex/contrib/groupbn/batch_norm.py -> build/lib/apex/contrib/groupbn\n",
            "creating build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/__init__.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_self_multihead_attn_norm_add_func.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_encdec_multihead_attn_norm_add_func.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/self_multihead_attn_func.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/mask_softmax_dropout_func.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_encdec_multihead_attn_func.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/encdec_multihead_attn.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/encdec_multihead_attn_func.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_self_multihead_attn_func.py -> build/lib/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/self_multihead_attn.py -> build/lib/apex/contrib/multihead_attn\n",
            "creating build/lib/apex/contrib/sparsity\n",
            "copying apex/contrib/sparsity/__init__.py -> build/lib/apex/contrib/sparsity\n",
            "copying apex/contrib/sparsity/sparse_masklib.py -> build/lib/apex/contrib/sparsity\n",
            "copying apex/contrib/sparsity/asp.py -> build/lib/apex/contrib/sparsity\n",
            "creating build/lib/apex/contrib/xentropy\n",
            "copying apex/contrib/xentropy/__init__.py -> build/lib/apex/contrib/xentropy\n",
            "copying apex/contrib/xentropy/softmax_xentropy.py -> build/lib/apex/contrib/xentropy\n",
            "creating build/lib/apex/amp/lists\n",
            "copying apex/amp/lists/__init__.py -> build/lib/apex/amp/lists\n",
            "copying apex/amp/lists/tensor_overrides.py -> build/lib/apex/amp/lists\n",
            "copying apex/amp/lists/functional_overrides.py -> build/lib/apex/amp/lists\n",
            "copying apex/amp/lists/torch_overrides.py -> build/lib/apex/amp/lists\n",
            "creating build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/usage.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/__init__.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/recurrentCell.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/embedding.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/__main__.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/misc.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/conv.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/softmax.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/activation.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/randomSample.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/blas.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/pooling.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/normalization.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/loss.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/convert.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/linear.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/utility.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/prof.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/dropout.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/base.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/pointwise.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/reduction.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/optim.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/output.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/index_slice_join_mutate.py -> build/lib/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/data.py -> build/lib/apex/pyprof/prof\n",
            "creating build/lib/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/__init__.py -> build/lib/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/db.py -> build/lib/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/__main__.py -> build/lib/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/parse.py -> build/lib/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/kernel.py -> build/lib/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/nvvp.py -> build/lib/apex/pyprof/parse\n",
            "creating build/lib/apex/pyprof/nvtx\n",
            "copying apex/pyprof/nvtx/__init__.py -> build/lib/apex/pyprof/nvtx\n",
            "copying apex/pyprof/nvtx/nvmarker.py -> build/lib/apex/pyprof/nvtx\n",
            "creating build/bdist.linux-x86_64\n",
            "creating build/bdist.linux-x86_64/egg\n",
            "creating build/bdist.linux-x86_64/egg/apex\n",
            "copying build/lib/apex/__init__.py -> build/bdist.linux-x86_64/egg/apex\n",
            "creating build/bdist.linux-x86_64/egg/apex/optimizers\n",
            "copying build/lib/apex/optimizers/__init__.py -> build/bdist.linux-x86_64/egg/apex/optimizers\n",
            "copying build/lib/apex/optimizers/fused_novograd.py -> build/bdist.linux-x86_64/egg/apex/optimizers\n",
            "copying build/lib/apex/optimizers/fused_adagrad.py -> build/bdist.linux-x86_64/egg/apex/optimizers\n",
            "copying build/lib/apex/optimizers/fused_sgd.py -> build/bdist.linux-x86_64/egg/apex/optimizers\n",
            "copying build/lib/apex/optimizers/fused_adam.py -> build/bdist.linux-x86_64/egg/apex/optimizers\n",
            "copying build/lib/apex/optimizers/fused_lamb.py -> build/bdist.linux-x86_64/egg/apex/optimizers\n",
            "creating build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/multiproc.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/__init__.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/LARC.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/sync_batchnorm_kernel.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/optimized_sync_batchnorm_kernel.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/sync_batchnorm.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/distributed.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "copying build/lib/apex/parallel/optimized_sync_batchnorm.py -> build/bdist.linux-x86_64/egg/apex/parallel\n",
            "creating build/bdist.linux-x86_64/egg/apex/reparameterization\n",
            "copying build/lib/apex/reparameterization/reparameterization.py -> build/bdist.linux-x86_64/egg/apex/reparameterization\n",
            "copying build/lib/apex/reparameterization/__init__.py -> build/bdist.linux-x86_64/egg/apex/reparameterization\n",
            "copying build/lib/apex/reparameterization/weight_norm.py -> build/bdist.linux-x86_64/egg/apex/reparameterization\n",
            "creating build/bdist.linux-x86_64/egg/apex/multi_tensor_apply\n",
            "copying build/lib/apex/multi_tensor_apply/__init__.py -> build/bdist.linux-x86_64/egg/apex/multi_tensor_apply\n",
            "copying build/lib/apex/multi_tensor_apply/multi_tensor_apply.py -> build/bdist.linux-x86_64/egg/apex/multi_tensor_apply\n",
            "creating build/bdist.linux-x86_64/egg/apex/contrib\n",
            "copying build/lib/apex/contrib/__init__.py -> build/bdist.linux-x86_64/egg/apex/contrib\n",
            "creating build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/__init__.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/distributed_fused_adam_v3.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/fused_sgd.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/distributed_fused_lamb.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/fused_adam.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/fp16_optimizer.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/distributed_fused_adam.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/distributed_fused_adam_v2.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "copying build/lib/apex/contrib/optimizers/fused_lamb.py -> build/bdist.linux-x86_64/egg/apex/contrib/optimizers\n",
            "creating build/bdist.linux-x86_64/egg/apex/contrib/groupbn\n",
            "copying build/lib/apex/contrib/groupbn/__init__.py -> build/bdist.linux-x86_64/egg/apex/contrib/groupbn\n",
            "copying build/lib/apex/contrib/groupbn/batch_norm.py -> build/bdist.linux-x86_64/egg/apex/contrib/groupbn\n",
            "creating build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/__init__.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/fast_self_multihead_attn_norm_add_func.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/fast_encdec_multihead_attn_norm_add_func.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/self_multihead_attn_func.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/mask_softmax_dropout_func.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/fast_encdec_multihead_attn_func.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/encdec_multihead_attn.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/encdec_multihead_attn_func.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/fast_self_multihead_attn_func.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "copying build/lib/apex/contrib/multihead_attn/self_multihead_attn.py -> build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn\n",
            "creating build/bdist.linux-x86_64/egg/apex/contrib/sparsity\n",
            "copying build/lib/apex/contrib/sparsity/__init__.py -> build/bdist.linux-x86_64/egg/apex/contrib/sparsity\n",
            "copying build/lib/apex/contrib/sparsity/sparse_masklib.py -> build/bdist.linux-x86_64/egg/apex/contrib/sparsity\n",
            "copying build/lib/apex/contrib/sparsity/asp.py -> build/bdist.linux-x86_64/egg/apex/contrib/sparsity\n",
            "creating build/bdist.linux-x86_64/egg/apex/contrib/xentropy\n",
            "copying build/lib/apex/contrib/xentropy/__init__.py -> build/bdist.linux-x86_64/egg/apex/contrib/xentropy\n",
            "copying build/lib/apex/contrib/xentropy/softmax_xentropy.py -> build/bdist.linux-x86_64/egg/apex/contrib/xentropy\n",
            "creating build/bdist.linux-x86_64/egg/apex/mlp\n",
            "copying build/lib/apex/mlp/__init__.py -> build/bdist.linux-x86_64/egg/apex/mlp\n",
            "copying build/lib/apex/mlp/mlp.py -> build/bdist.linux-x86_64/egg/apex/mlp\n",
            "creating build/bdist.linux-x86_64/egg/apex/fp16_utils\n",
            "copying build/lib/apex/fp16_utils/__init__.py -> build/bdist.linux-x86_64/egg/apex/fp16_utils\n",
            "copying build/lib/apex/fp16_utils/fp16util.py -> build/bdist.linux-x86_64/egg/apex/fp16_utils\n",
            "copying build/lib/apex/fp16_utils/fp16_optimizer.py -> build/bdist.linux-x86_64/egg/apex/fp16_utils\n",
            "copying build/lib/apex/fp16_utils/loss_scaler.py -> build/bdist.linux-x86_64/egg/apex/fp16_utils\n",
            "creating build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/__init__.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/amp.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/__version__.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/utils.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/opt.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/_amp_state.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "creating build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib/apex/amp/lists/__init__.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib/apex/amp/lists/tensor_overrides.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib/apex/amp/lists/functional_overrides.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib/apex/amp/lists/torch_overrides.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib/apex/amp/compat.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/frontend.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/_process_optimizer.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/rnn_compat.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/handle.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/scaler.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/wrap.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib/apex/amp/_initialize.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "creating build/bdist.linux-x86_64/egg/apex/pyprof\n",
            "copying build/lib/apex/pyprof/__init__.py -> build/bdist.linux-x86_64/egg/apex/pyprof\n",
            "creating build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/usage.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/__init__.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/recurrentCell.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/embedding.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/__main__.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/misc.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/conv.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/softmax.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/activation.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/randomSample.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/blas.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/pooling.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/normalization.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/loss.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/convert.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/linear.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/utility.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/prof.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/dropout.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/base.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/pointwise.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/reduction.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/optim.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/output.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/index_slice_join_mutate.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "copying build/lib/apex/pyprof/prof/data.py -> build/bdist.linux-x86_64/egg/apex/pyprof/prof\n",
            "creating build/bdist.linux-x86_64/egg/apex/pyprof/parse\n",
            "copying build/lib/apex/pyprof/parse/__init__.py -> build/bdist.linux-x86_64/egg/apex/pyprof/parse\n",
            "copying build/lib/apex/pyprof/parse/db.py -> build/bdist.linux-x86_64/egg/apex/pyprof/parse\n",
            "copying build/lib/apex/pyprof/parse/__main__.py -> build/bdist.linux-x86_64/egg/apex/pyprof/parse\n",
            "copying build/lib/apex/pyprof/parse/parse.py -> build/bdist.linux-x86_64/egg/apex/pyprof/parse\n",
            "copying build/lib/apex/pyprof/parse/kernel.py -> build/bdist.linux-x86_64/egg/apex/pyprof/parse\n",
            "copying build/lib/apex/pyprof/parse/nvvp.py -> build/bdist.linux-x86_64/egg/apex/pyprof/parse\n",
            "creating build/bdist.linux-x86_64/egg/apex/pyprof/nvtx\n",
            "copying build/lib/apex/pyprof/nvtx/__init__.py -> build/bdist.linux-x86_64/egg/apex/pyprof/nvtx\n",
            "copying build/lib/apex/pyprof/nvtx/nvmarker.py -> build/bdist.linux-x86_64/egg/apex/pyprof/nvtx\n",
            "creating build/bdist.linux-x86_64/egg/apex/RNN\n",
            "copying build/lib/apex/RNN/models.py -> build/bdist.linux-x86_64/egg/apex/RNN\n",
            "copying build/lib/apex/RNN/__init__.py -> build/bdist.linux-x86_64/egg/apex/RNN\n",
            "copying build/lib/apex/RNN/RNNBackend.py -> build/bdist.linux-x86_64/egg/apex/RNN\n",
            "copying build/lib/apex/RNN/cells.py -> build/bdist.linux-x86_64/egg/apex/RNN\n",
            "creating build/bdist.linux-x86_64/egg/apex/normalization\n",
            "copying build/lib/apex/normalization/__init__.py -> build/bdist.linux-x86_64/egg/apex/normalization\n",
            "copying build/lib/apex/normalization/fused_layer_norm.py -> build/bdist.linux-x86_64/egg/apex/normalization\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/optimizers/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/optimizers/fused_novograd.py to fused_novograd.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/optimizers/fused_adagrad.py to fused_adagrad.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/optimizers/fused_sgd.py to fused_sgd.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/optimizers/fused_adam.py to fused_adam.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/optimizers/fused_lamb.py to fused_lamb.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/multiproc.py to multiproc.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/LARC.py to LARC.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/sync_batchnorm_kernel.py to sync_batchnorm_kernel.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/optimized_sync_batchnorm_kernel.py to optimized_sync_batchnorm_kernel.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/sync_batchnorm.py to sync_batchnorm.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/distributed.py to distributed.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/parallel/optimized_sync_batchnorm.py to optimized_sync_batchnorm.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/reparameterization/reparameterization.py to reparameterization.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/reparameterization/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/reparameterization/weight_norm.py to weight_norm.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/multi_tensor_apply/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/multi_tensor_apply/multi_tensor_apply.py to multi_tensor_apply.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/distributed_fused_adam_v3.py to distributed_fused_adam_v3.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/fused_sgd.py to fused_sgd.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/distributed_fused_lamb.py to distributed_fused_lamb.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/fused_adam.py to fused_adam.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/fp16_optimizer.py to fp16_optimizer.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/distributed_fused_adam.py to distributed_fused_adam.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/distributed_fused_adam_v2.py to distributed_fused_adam_v2.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/optimizers/fused_lamb.py to fused_lamb.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/groupbn/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/groupbn/batch_norm.py to batch_norm.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/fast_self_multihead_attn_norm_add_func.py to fast_self_multihead_attn_norm_add_func.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/fast_encdec_multihead_attn_norm_add_func.py to fast_encdec_multihead_attn_norm_add_func.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/self_multihead_attn_func.py to self_multihead_attn_func.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/mask_softmax_dropout_func.py to mask_softmax_dropout_func.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/fast_encdec_multihead_attn_func.py to fast_encdec_multihead_attn_func.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/encdec_multihead_attn.py to encdec_multihead_attn.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/encdec_multihead_attn_func.py to encdec_multihead_attn_func.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/fast_self_multihead_attn_func.py to fast_self_multihead_attn_func.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/multihead_attn/self_multihead_attn.py to self_multihead_attn.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/sparsity/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/sparsity/sparse_masklib.py to sparse_masklib.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/sparsity/asp.py to asp.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/xentropy/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/contrib/xentropy/softmax_xentropy.py to softmax_xentropy.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/mlp/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/mlp/mlp.py to mlp.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/fp16_utils/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/fp16_utils/fp16util.py to fp16util.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/fp16_utils/fp16_optimizer.py to fp16_optimizer.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/fp16_utils/loss_scaler.py to loss_scaler.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/amp.py to amp.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/__version__.py to __version__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/utils.py to utils.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/opt.py to opt.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/_amp_state.py to _amp_state.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/lists/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/lists/tensor_overrides.py to tensor_overrides.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/lists/functional_overrides.py to functional_overrides.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/lists/torch_overrides.py to torch_overrides.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/compat.py to compat.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/frontend.py to frontend.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/_process_optimizer.py to _process_optimizer.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/rnn_compat.py to rnn_compat.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/handle.py to handle.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/scaler.py to scaler.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/wrap.py to wrap.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/amp/_initialize.py to _initialize.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/usage.py to usage.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/recurrentCell.py to recurrentCell.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/embedding.py to embedding.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/__main__.py to __main__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/misc.py to misc.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/conv.py to conv.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/softmax.py to softmax.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/activation.py to activation.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/randomSample.py to randomSample.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/blas.py to blas.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/pooling.py to pooling.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/normalization.py to normalization.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/loss.py to loss.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/convert.py to convert.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/linear.py to linear.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/utility.py to utility.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/prof.py to prof.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/dropout.py to dropout.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/base.py to base.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/pointwise.py to pointwise.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/reduction.py to reduction.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/optim.py to optim.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/output.py to output.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/index_slice_join_mutate.py to index_slice_join_mutate.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/prof/data.py to data.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/parse/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/parse/db.py to db.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/parse/__main__.py to __main__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/parse/parse.py to parse.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/parse/kernel.py to kernel.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/parse/nvvp.py to nvvp.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/nvtx/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/pyprof/nvtx/nvmarker.py to nvmarker.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/RNN/models.py to models.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/RNN/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/RNN/RNNBackend.py to RNNBackend.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/RNN/cells.py to cells.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/normalization/__init__.py to __init__.cpython-36.pyc\n",
            "byte-compiling build/bdist.linux-x86_64/egg/apex/normalization/fused_layer_norm.py to fused_layer_norm.cpython-36.pyc\n",
            "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying apex.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying apex.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying apex.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "copying apex.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
            "creating dist\n",
            "creating 'dist/apex-0.1-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
            "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
            "Processing apex-0.1-py3.6.egg\n",
            "creating /usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6.egg\n",
            "Extracting apex-0.1-py3.6.egg to /usr/local/lib/python3.6/dist-packages\n",
            "Adding apex 0.1 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6.egg\n",
            "Processing dependencies for apex==0.1\n",
            "Finished processing dependencies for apex==0.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING: Skipping apex as it is not installed.\n",
            "Cloning into 'apex'...\n",
            "warning: redirecting to https://github.com/nvidia/apex.git/\n",
            "setup.py:67: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!\n",
            "  warnings.warn(\"Option --pyprof not specified. Not installing PyProf dependencies!\")\n",
            "zip_safe flag not set; analyzing archive contents...\n",
            "apex.pyprof.nvtx.__pycache__.nvmarker.cpython-36: module references __file__\n",
            "apex.pyprof.nvtx.__pycache__.nvmarker.cpython-36: module references __path__\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lV0K5d6PhATz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#%%shell\n",
        "#pip install future typing numpy awscli\n",
        "#pip install https://download.pytorch.org/whl/cu100/torch-1.1.0-cp36-cp36m-linux_x86_64.whl\n",
        "#pip install https://download.pytorch.org/whl/cu100/torchvision-0.3.0-cp36-cp36m-linux_x86_64.whl"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoUpXWTCpzDG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#%%shell\n",
        "#pip install pillow==5.0.0"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCNHoekMbmRh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "6dba6203-7e07-4bd3-add2-23644ccc6e7b"
      },
      "source": [
        "!pip install yacs"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yacs\n",
            "  Downloading https://files.pythonhosted.org/packages/38/4f/fe9a4d472aa867878ce3bb7efb16654c5d63672b86dc0e6e953a67018433/yacs-0.1.8-py3-none-any.whl\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.6/dist-packages (from yacs) (3.13)\n",
            "Installing collected packages: yacs\n",
            "Successfully installed yacs-0.1.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqIjdgsj36mh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mv '/content/realtime_panoptic/realtime_panoptic' '/usr/local/lib/python3.6/dist-packages'"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4wER3oGjsaR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This script provides a demo inference a model trained on Cityscapes dataset.\n",
        "import warnings\n",
        "import argparse\n",
        "import torch\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from torchvision.models.detection.image_list import ImageList\n",
        "\n",
        "from realtime_panoptic.models.rt_pano_net import RTPanoNet\n",
        "from realtime_panoptic.config import cfg\n",
        "import realtime_panoptic.data.panoptic_transform as P\n",
        "from realtime_panoptic.utils.visualization import visualize_segmentation_image,visualize_detection_image"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOG8xOeiiSAS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cityscapes_colormap = np.array([\n",
        " [128,  64, 128],\n",
        " [244,  35, 232],\n",
        " [ 70,  70,  70],\n",
        " [102, 102, 156],\n",
        " [190, 153, 153],\n",
        " [153, 153, 153],\n",
        " [250 ,170,  30],\n",
        " [220, 220,   0],\n",
        " [107, 142,  35],\n",
        " [152, 251, 152],\n",
        " [ 70, 130, 180],\n",
        " [220,  20,  60],\n",
        " [255,   0,   0],\n",
        " [  0,   0, 142],\n",
        " [  0,   0,  70],\n",
        " [  0,  60, 100],\n",
        " [  0,  80, 100],\n",
        " [  0,   0, 230],\n",
        " [119,  11,  32],\n",
        " [  0,   0,   0]])\n",
        "\n",
        "cityscapes_instance_label_name = ['person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle']\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFJGwLwTisjq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cfg.merge_from_file('/content/realtime_panoptic/configs/demo_config.yaml')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bb8dM3xejMVd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "68f43cf2-109d-4c54-af1a-17d77979a823"
      },
      "source": [
        "cfg.model.backbone"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'R-50-FPN-RETINANET'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQaFWiyQjHjL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "38b4736e-9e9c-4de0-80da-8cd4b0892182"
      },
      "source": [
        "model = RTPanoNet(\n",
        "    backbone=cfg.model.backbone, \n",
        "    num_classes=cfg.model.panoptic.num_classes,\n",
        "    things_num_classes=cfg.model.panoptic.num_thing_classes,\n",
        "    pre_nms_thresh=cfg.model.panoptic.pre_nms_thresh,\n",
        "    pre_nms_top_n=cfg.model.panoptic.pre_nms_top_n,\n",
        "    nms_thresh=cfg.model.panoptic.nms_thresh,\n",
        "    fpn_post_nms_top_n=cfg.model.panoptic.fpn_post_nms_top_n,\n",
        "    instance_id_range=cfg.model.panoptic.instance_id_range)\n",
        "device = 'cuda'\n",
        "model.to(device)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RTPanoNet(\n",
              "  (backbone): ResNetWithModifiedFPN(\n",
              "    (body): IntermediateLayerGetter(\n",
              "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "      (bn1): FrozenBatchNorm2d(64)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "      (layer1): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (1): FrozenBatchNorm2d(256)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(64)\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(64)\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(256)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer2): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(512)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(128)\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(128)\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(512)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer3): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(1024)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (4): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (5): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(256)\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(256)\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(1024)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (layer4): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048)\n",
              "          (relu): ReLU(inplace=True)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d(2048)\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d(512)\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d(512)\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d(2048)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (fpn): FeaturePyramidNetwork(\n",
              "      (inner_blocks): ModuleList(\n",
              "        (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (2): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (layer_blocks): ModuleList(\n",
              "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      )\n",
              "      (extra_blocks): LastLevelP6P7(\n",
              "        (p6): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        (p7): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (panoptic_head): PanopticHead(\n",
              "    (cls_tower): Sequential(\n",
              "      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (2): ReLU()\n",
              "      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (5): ReLU()\n",
              "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (7): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (8): ReLU()\n",
              "      (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (10): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (11): ReLU()\n",
              "    )\n",
              "    (bbox_tower): Sequential(\n",
              "      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (2): ReLU()\n",
              "      (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (5): ReLU()\n",
              "      (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (7): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (8): ReLU()\n",
              "      (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (10): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (11): ReLU()\n",
              "    )\n",
              "    (cls_logits): Conv2d(640, 19, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (box_cls_logits): Conv2d(128, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (bbox_pred): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (centerness): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (levelness): Conv2d(1280, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (scales): ModuleList(\n",
              "      (0): Scale()\n",
              "      (1): Scale()\n",
              "      (2): Scale()\n",
              "      (3): Scale()\n",
              "      (4): Scale()\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtYnPpvgjNu8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d55f2eec-8261-4f4e-dae9-c3b935519dfb"
      },
      "source": [
        "model.load_state_dict(torch.load('/content/cvpr_realtime_pano_cityscapes_standalone_no_prefix.pth'))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXCqqW60mgDi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2844d440-75d8-467a-b9c3-119320538239"
      },
      "source": [
        "print(model)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RTPanoNet(\n",
            "  (backbone): ResNetWithModifiedFPN(\n",
            "    (body): IntermediateLayerGetter(\n",
            "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
            "      (bn1): FrozenBatchNorm2d(64)\n",
            "      (relu): ReLU(inplace=True)\n",
            "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
            "      (layer1): Sequential(\n",
            "        (0): Bottleneck(\n",
            "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(64)\n",
            "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(64)\n",
            "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(256)\n",
            "          (relu): ReLU(inplace=True)\n",
            "          (downsample): Sequential(\n",
            "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): FrozenBatchNorm2d(256)\n",
            "          )\n",
            "        )\n",
            "        (1): Bottleneck(\n",
            "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(64)\n",
            "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(64)\n",
            "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(256)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (2): Bottleneck(\n",
            "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(64)\n",
            "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(64)\n",
            "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(256)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (layer2): Sequential(\n",
            "        (0): Bottleneck(\n",
            "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(128)\n",
            "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(128)\n",
            "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(512)\n",
            "          (relu): ReLU(inplace=True)\n",
            "          (downsample): Sequential(\n",
            "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): FrozenBatchNorm2d(512)\n",
            "          )\n",
            "        )\n",
            "        (1): Bottleneck(\n",
            "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(128)\n",
            "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(128)\n",
            "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(512)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (2): Bottleneck(\n",
            "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(128)\n",
            "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(128)\n",
            "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(512)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (3): Bottleneck(\n",
            "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(128)\n",
            "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(128)\n",
            "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(512)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (layer3): Sequential(\n",
            "        (0): Bottleneck(\n",
            "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(256)\n",
            "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(256)\n",
            "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(1024)\n",
            "          (relu): ReLU(inplace=True)\n",
            "          (downsample): Sequential(\n",
            "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): FrozenBatchNorm2d(1024)\n",
            "          )\n",
            "        )\n",
            "        (1): Bottleneck(\n",
            "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(256)\n",
            "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(256)\n",
            "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(1024)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (2): Bottleneck(\n",
            "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(256)\n",
            "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(256)\n",
            "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(1024)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (3): Bottleneck(\n",
            "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(256)\n",
            "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(256)\n",
            "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(1024)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (4): Bottleneck(\n",
            "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(256)\n",
            "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(256)\n",
            "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(1024)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (5): Bottleneck(\n",
            "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(256)\n",
            "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(256)\n",
            "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(1024)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "      (layer4): Sequential(\n",
            "        (0): Bottleneck(\n",
            "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(512)\n",
            "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(512)\n",
            "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(2048)\n",
            "          (relu): ReLU(inplace=True)\n",
            "          (downsample): Sequential(\n",
            "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "            (1): FrozenBatchNorm2d(2048)\n",
            "          )\n",
            "        )\n",
            "        (1): Bottleneck(\n",
            "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(512)\n",
            "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(512)\n",
            "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(2048)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "        (2): Bottleneck(\n",
            "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn1): FrozenBatchNorm2d(512)\n",
            "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn2): FrozenBatchNorm2d(512)\n",
            "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn3): FrozenBatchNorm2d(2048)\n",
            "          (relu): ReLU(inplace=True)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (fpn): FeaturePyramidNetwork(\n",
            "      (inner_blocks): ModuleList(\n",
            "        (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "        (1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "        (2): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "      )\n",
            "      (layer_blocks): ModuleList(\n",
            "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      )\n",
            "      (extra_blocks): LastLevelP6P7(\n",
            "        (p6): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "        (p7): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (panoptic_head): PanopticHead(\n",
            "    (cls_tower): Sequential(\n",
            "      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (1): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
            "      (2): ReLU()\n",
            "      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
            "      (5): ReLU()\n",
            "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
            "      (8): ReLU()\n",
            "      (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (10): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
            "      (11): ReLU()\n",
            "    )\n",
            "    (bbox_tower): Sequential(\n",
            "      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
            "      (2): ReLU()\n",
            "      (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (4): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
            "      (5): ReLU()\n",
            "      (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (7): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
            "      (8): ReLU()\n",
            "      (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "      (10): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
            "      (11): ReLU()\n",
            "    )\n",
            "    (cls_logits): Conv2d(640, 19, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (box_cls_logits): Conv2d(128, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (bbox_pred): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (centerness): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (levelness): Conv2d(1280, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (scales): ModuleList(\n",
            "      (0): Scale()\n",
            "      (1): Scale()\n",
            "      (2): Scale()\n",
            "      (3): Scale()\n",
            "      (4): Scale()\n",
            "    )\n",
            "  )\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRE7Y_XduJ1P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "29698870-acba-45a2-9e40-95f90e4e0a98"
      },
      "source": [
        "# Prepare for model inference.\n",
        "input_image = Image.open('/content/realtime_panoptic/media/figs/test.png')\n",
        "data = {'image': input_image}\n",
        "# data pre-processing\n",
        "normalize_transform = P.Normalize(mean=cfg.input.pixel_mean, std=cfg.input.pixel_std, to_bgr255=cfg.input.to_bgr255)\n",
        "transform = P.Compose([\n",
        "    P.ToTensor(),\n",
        "    normalize_transform,\n",
        "])\n",
        "data = transform(data)\n",
        "print(\"Done with data preparation and model configuration.\")\n",
        "input_image_list = ImageList([data['image'].to(device)], image_sizes=[input_image.size[::-1]])\n",
        "input = torch.stack(input_image_list.tensors)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done with data preparation and model configuration.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxS-NJ88uRnh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3da3c9c0-812e-47ad-e2d8-f766dfe933a7"
      },
      "source": [
        "input.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 1024, 2048])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKcPe9Dzt7uH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "5ad97939-b0c7-426c-b2e6-c652b63d122d"
      },
      "source": [
        "!wget 'https://storage.googleapis.com/groundai-web-prod/media%2Fusers%2Fuser_272722%2Fproject_255779%2Fimages%2Ftraining_0.jpg'"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-17 11:25:30--  https://storage.googleapis.com/groundai-web-prod/media%2Fusers%2Fuser_272722%2Fproject_255779%2Fimages%2Ftraining_0.jpg\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.142.128, 74.125.195.128, 74.125.20.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.142.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 166289 (162K) [image/jpeg]\n",
            "Saving to: ‘media%2Fusers%2Fuser_272722%2Fproject_255779%2Fimages%2Ftraining_0.jpg’\n",
            "\n",
            "\r          media%2Fu   0%[                    ]       0  --.-KB/s               \rmedia%2Fusers%2Fuse 100%[===================>] 162.39K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2020-08-17 11:25:30 (153 MB/s) - ‘media%2Fusers%2Fuser_272722%2Fproject_255779%2Fimages%2Ftraining_0.jpg’ saved [166289/166289]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wLsL8AmqZ1I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "2bca82ee-ea9c-4f69-e303-f7de42539309"
      },
      "source": [
        "# Prepare for model inference.\n",
        "model.eval()\n",
        "input_image = Image.open('/content/media%2Fusers%2Fuser_272722%2Fproject_255779%2Fimages%2Ftraining_0.jpg')\n",
        "data = {'image': input_image}\n",
        "# data pre-processing\n",
        "normalize_transform = P.Normalize(mean=cfg.input.pixel_mean, std=cfg.input.pixel_std, to_bgr255=cfg.input.to_bgr255)\n",
        "transform = P.Compose([\n",
        "    P.ToTensor(),\n",
        "    normalize_transform,\n",
        "])\n",
        "data = transform(data)\n",
        "print(\"Done with data preparation and model configuration.\")\n",
        "with torch.no_grad():\n",
        "    input_image_list = ImageList([data['image'].to(device)], image_sizes=[input_image.size[::-1]])\n",
        "    panoptic_result, _ = model.forward(input_image_list)\n",
        "    print(\"Done with model inference.\")\n",
        "    print(\"Process and visualizing the outputs...\")\n",
        "    instance_detection = [o.to('cpu') for o in panoptic_result[\"instance_segmentation_result\"]]\n",
        "    semseg_logics = [o.to('cpu') for o in panoptic_result[\"semantic_segmentation_result\"]]\n",
        "    semseg_prob = [torch.argmax(semantic_logit , dim=0) for semantic_logit in  semseg_logics]\n",
        "\n",
        "    seg_vis = visualize_segmentation_image(semseg_prob[0], input_image, cityscapes_colormap)\n",
        "    Image.fromarray(seg_vis.astype('uint8')).save('semantic_segmentation_result.jpg')\n",
        "    print(\"Saved semantic segmentation visualization in semantic_segmentation_result.jpg\")\n",
        "    det_vis = visualize_detection_image(instance_detection[0], input_image, cityscapes_instance_label_name)\n",
        "    Image.fromarray(det_vis.astype('uint8')).save('instance_segmentation_result.jpg')\n",
        "    print(\"Saved instance segmentation visualization in instance_segmentation_result.jpg\")\n",
        "    print(\"Demo finished.\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done with data preparation and model configuration.\n",
            "Done with model inference.\n",
            "Process and visualizing the outputs...\n",
            "Saved semantic segmentation visualization in semantic_segmentation_result.jpg\n",
            "Saved instance segmentation visualization in instance_segmentation_result.jpg\n",
            "Demo finished.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6vhVH3lKv2g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "8c302543-b6da-48af-a460-50bc9d78bc72"
      },
      "source": [
        "from realtime_panoptic.utils.panoptic_vis import panoptic_visualization\n",
        "pan = panoptic_visualization(instance_detection[0], semseg_prob[0], input_image)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/realtime_panoptic/utils/panoptic_vis.py:139: RuntimeWarning: overflow encountered in exp\n",
            "  z = 1/(1 + np.exp(x))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2j52Ee_yLVcC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "c3a3226d-8cef-484b-a5f6-128ee2cb14e9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(pan)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7effe640b5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B92-Zk7tjpD2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "1f94fcd0-0284-4761-bbb8-dd35ce24d7d1"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(det_vis)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6ad819b7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euIBK_tf_ElL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "a13fc774-e4a5-4413-db5b-8f62c1dbf941"
      },
      "source": [
        "plt.imshow(seg_vis)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6ad80c6898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lo3wKpe8P8ji",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import copy\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "DETECTRON_PALETTE = np.array(\n",
        "    [\n",
        "        0.000, 0.447, 0.741,\n",
        "        0.850, 0.325, 0.098,\n",
        "        0.929, 0.694, 0.125,\n",
        "        0.494, 0.184, 0.556,\n",
        "        0.466, 0.674, 0.188,\n",
        "        0.301, 0.745, 0.933,\n",
        "        0.635, 0.078, 0.184,\n",
        "        0.300, 0.300, 0.300,\n",
        "        0.600, 0.600, 0.600,\n",
        "        1.000, 0.000, 0.000,\n",
        "        1.000, 0.500, 0.000,\n",
        "        0.749, 0.749, 0.000,\n",
        "        0.000, 1.000, 0.000,\n",
        "        0.000, 0.000, 1.000,\n",
        "        0.667, 0.000, 1.000,\n",
        "        0.333, 0.333, 0.000,\n",
        "        0.333, 0.667, 0.000,\n",
        "        0.333, 1.000, 0.000,\n",
        "        0.667, 0.333, 0.000,\n",
        "        0.667, 0.667, 0.000,\n",
        "        0.667, 1.000, 0.000,\n",
        "        1.000, 0.333, 0.000,\n",
        "        1.000, 0.667, 0.000,\n",
        "        1.000, 1.000, 0.000,\n",
        "        0.000, 0.333, 0.500,\n",
        "        0.000, 0.667, 0.500,\n",
        "        0.000, 1.000, 0.500,\n",
        "        0.333, 0.000, 0.500,\n",
        "        0.333, 0.333, 0.500,\n",
        "        0.333, 0.667, 0.500,\n",
        "        0.333, 1.000, 0.500,\n",
        "        0.667, 0.000, 0.500,\n",
        "        0.667, 0.333, 0.500,\n",
        "        0.667, 0.667, 0.500,\n",
        "        0.667, 1.000, 0.500,\n",
        "        1.000, 0.000, 0.500,\n",
        "        1.000, 0.333, 0.500,\n",
        "        1.000, 0.667, 0.500,\n",
        "        1.000, 1.000, 0.500,\n",
        "        0.000, 0.333, 1.000,\n",
        "        0.000, 0.667, 1.000,\n",
        "        0.000, 1.000, 1.000,\n",
        "        0.333, 0.000, 1.000,\n",
        "        0.333, 0.333, 1.000,\n",
        "        0.333, 0.667, 1.000,\n",
        "        0.333, 1.000, 1.000,\n",
        "        0.667, 0.000, 1.000,\n",
        "        0.667, 0.333, 1.000,\n",
        "        0.667, 0.667, 1.000,\n",
        "        0.667, 1.000, 1.000,\n",
        "        1.000, 0.000, 1.000,\n",
        "        1.000, 0.333, 1.000,\n",
        "        1.000, 0.667, 1.000,\n",
        "        0.167, 0.000, 0.000,\n",
        "        0.333, 0.000, 0.000,\n",
        "        0.500, 0.000, 0.000,\n",
        "        0.667, 0.000, 0.000,\n",
        "        0.833, 0.000, 0.000,\n",
        "        1.000, 0.000, 0.000,\n",
        "        0.000, 0.167, 0.000,\n",
        "        0.000, 0.333, 0.000,\n",
        "        0.000, 0.500, 0.000,\n",
        "        0.000, 0.667, 0.000,\n",
        "        0.000, 0.833, 0.000,\n",
        "        0.000, 1.000, 0.000,\n",
        "        0.000, 0.000, 0.167,\n",
        "        0.000, 0.000, 0.333,\n",
        "        0.000, 0.000, 0.500,\n",
        "        0.000, 0.000, 0.667,\n",
        "        0.000, 0.000, 0.833,\n",
        "        0.000, 0.000, 1.000,\n",
        "        0.000, 0.000, 0.000,\n",
        "        0.143, 0.143, 0.143,\n",
        "        0.286, 0.286, 0.286,\n",
        "        0.429, 0.429, 0.429,\n",
        "        0.571, 0.571, 0.571,\n",
        "        0.714, 0.714, 0.714,\n",
        "        0.857, 0.857, 0.857,\n",
        "        1.000, 1.000, 1.000\n",
        "    ]\n",
        ").astype(np.float32).reshape(-1, 3) * 255"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHAfLCkTQYvj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def random_color(base, max_dist=30):\n",
        "    \"\"\"Generate random color close to a given base color.\n",
        "    Parameters:\n",
        "    -----------\n",
        "    base: array_like\n",
        "        Base color for random color generation\n",
        "    max_dist: int\n",
        "        Max distance from generated color to base color on all RGB axis.\n",
        "    \n",
        "    Returns:\n",
        "    --------\n",
        "    random_color: tuple\n",
        "        3 channel random color around the given base color.\n",
        "    \"\"\"\n",
        "    base = np.array(base)\n",
        "    new_color = base + np.random.randint(low=-max_dist, high=max_dist + 1, size=3)\n",
        "    return tuple(np.maximum(0, np.minimum(255, new_color)))\n",
        "\n",
        "def draw_mask(im, mask, alpha=0.5, color=None):\n",
        "    \"\"\"Overlay a mask on top of the image.\n",
        "    Parameters:\n",
        "    -----------\n",
        "    im: array_like\n",
        "        A 3-channel uint8 image\n",
        "    mask: array_like\n",
        "        A binary 1-channel image of the same size\n",
        "    color: bool\n",
        "        If None, will choose automatically\n",
        "    alpha: float\n",
        "        mask intensity\n",
        "    Returns:\n",
        "    --------\n",
        "    im: np.array\n",
        "        Image overlaid by masks.\n",
        "    color: list\n",
        "        Color used for masks. \n",
        "    \"\"\"\n",
        "    if color is None:\n",
        "        color = DETECTRON_PALETTE[np.random.choice(len(DETECTRON_PALETTE))][::-1]\n",
        "    color = np.asarray(color, dtype=np.int64)\n",
        "    im = np.where(np.repeat((mask > 0)[:, :, None], 3, axis=2), im * (1 - alpha) + color * alpha, im)\n",
        "    im = im.astype('uint8')\n",
        "    return im, color.tolist()"
      ],
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fW5N4dgvP0gc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "def twotothree(x):\n",
        "  z = np.concatenate((x.reshape(1024, 2048, 1),x.reshape(1024, 2048, 1),x.reshape(1024, 2048, 1)),axis=2)\n",
        "  return z\n",
        "def sigmoid(x):\n",
        "  z = 1/(1 + np.exp(x)) \n",
        "  return z\n",
        "def getBordered(image, width):\n",
        "    bg = np.zeros(image.shape)\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2YCR_CB)[...,0]\n",
        "    contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
        "    biggest = 0\n",
        "    bigcontour = None\n",
        "    for contour in contours:\n",
        "        area = cv2.contourArea(contour) \n",
        "        if area > biggest:\n",
        "            biggest = area\n",
        "            bigcontour = contour\n",
        "    return cv2.drawContours(bg, [bigcontour], 0, (255, 255, 255), width).astype(bool) \n",
        "def masked_image(predictions, original_image, label_id_to_names, y_instance, fade_weight=0.8):\n",
        "    \"\"\"Log a single detection result for visualization.\n",
        "    \n",
        "    Overlays predicted classes on top of raw RGB image.\n",
        "    Parameters:\n",
        "    -----------\n",
        "    predictions: torch.cuda.LongTensor\n",
        "        Per-pixel predicted class ID's for a single input image\n",
        "        Shape: (H, W)\n",
        "    original_image: np.array\n",
        "        HxWx3 original image. or None\n",
        "    label_id_to_names: list\n",
        "        list of class names for instance labels\n",
        "    fade_weight: float, default: 0.8\n",
        "        Visualization is fade_weight * original_image + (1 - fade_weight) * predictions\n",
        "    Returns:\n",
        "    -------\n",
        "    visualized_image: np.array\n",
        "        Visualized image with detection results.\n",
        "    \"\"\"\n",
        "\n",
        "    # Load raw image using provided dataset and index\n",
        "    # ``images_numpy`` has shape (H, W, 3)\n",
        "    # ``images_numpy`` has shape (H, W,3)\n",
        "    if not isinstance(original_image, np.ndarray):\n",
        "        original_image = np.array(original_image)\n",
        "    original_image_height, original_image_width,_ = original_image.shape\n",
        "\n",
        "    # overlay_boxes\n",
        "    visualized_image = copy.copy(np.array(original_image))\n",
        "\n",
        "    labels = predictions.get_field(\"labels\").to(\"cpu\")\n",
        "    boxes = predictions.bbox\n",
        "\n",
        "    dtype = labels.dtype\n",
        "    palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1]).to(dtype)\n",
        "    colors = labels[:, None] * palette\n",
        "    colors = (colors % 255).numpy().astype(\"uint8\")\n",
        "    masks = None\n",
        "    if predictions.has_field(\"mask\"):\n",
        "        masks = predictions.get_field(\"mask\")\n",
        "    else:\n",
        "        masks = [None] * len(boxes)\n",
        "    # overlay_class_names_and_score\n",
        "    if predictions.has_field(\"scores\"):\n",
        "        scores = predictions.get_field(\"scores\").tolist()\n",
        "    else:\n",
        "        scores = [1.0] * len(boxes)\n",
        "    # predicted label starts from 1 as 0 is reserved for background.\n",
        "    label_names = [label_id_to_names[i-1] for i in labels.tolist()]\n",
        "\n",
        "    text_template = \"{}: {:.2f}\"\n",
        "    visualized_image = np.zeros(visualized_image.shape)\n",
        "    edge = np.zeros(visualized_image.shape)\n",
        "    for box, color, score, mask, label in zip(boxes, colors, scores, masks, label_names):\n",
        "        if score < 0.5:\n",
        "            continue\n",
        "        box = box.to(torch.int64)\n",
        "        color = random_color(color)\n",
        "        color = tuple(map(int, color))\n",
        "        if mask is not None:\n",
        "            thresh = (mask > 0.5).cpu().numpy().astype('uint8')\n",
        "            visualized_image, color = draw_mask(visualized_image, thresh)\n",
        "            #visualized_image = (sigmoid(visualized_image) + sigmoid(y_instance))*(2*visualized_image)\n",
        "            #visualized_image = visualized_image.astype('uint8')\n",
        "            edge = edge + getBordered(twotothree(thresh), 3)\n",
        "\n",
        "\n",
        "        #x, y = box[:2]\n",
        "        #s = text_template.format(label, score)\n",
        "        #cv2.putText(visualized_image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1)\n",
        "        #print(thresh.shape)\n",
        "        #print(color)\n",
        "        #top_left, bottom_right = box[:2].tolist(), box[2:].tolist()\n",
        "        #visualized_image = cv2.rectangle(visualized_image, tuple(top_left), tuple(bottom_right), tuple(color), 1)\n",
        "    return (visualized_image, edge.astype('uint8'), thresh)\n",
        "x = masked_image(instance_detection[0], input_image, cityscapes_instance_label_name, y_instance)"
      ],
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMG8so5kbvVt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "c194ca4f-d164-44be-9156-7bd2bf3ecd3c"
      },
      "source": [
        "plt.imshow(x[1]*255, cmap='gray')"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6a2c27ab38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lsz21ZyYSM31",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def seg_image(predictions, original_image, colormap, fade_weight=0.5):\n",
        "    \"\"\"Log a single segmentation result for visualization using a colormap.\n",
        "    \n",
        "    Overlays predicted classes on top of raw RGB image if given.\n",
        "    Parameters:\n",
        "    -----------\n",
        "    predictions: torch.cuda.LongTensor\n",
        "        Per-pixel predicted class ID's for a single input image\n",
        "        Shape: (H, W)\n",
        "    original_image: np.array\n",
        "        HxWx3 original image. or None\n",
        "    colormap: np.array\n",
        "        (N+1)x3 array colormap,where N+1 equals to the number of classes.\n",
        "    fade_weight: float, default: 0.8\n",
        "        Visualization is fade_weight * original_image + (1 - fade_weight) * predictions\n",
        "    Returns:\n",
        "    --------\n",
        "    visualized_image: np.array\n",
        "        Semantic semantic visualization color coded by classes.\n",
        "        The visualization will be overlaid on a the RGB image if given. \n",
        "    \"\"\"\n",
        "\n",
        "    # ``original_image`` has shape (H, W,3)\n",
        "    if not isinstance(original_image, np.ndarray):\n",
        "        original_image = np.array(original_image)\n",
        "    original_image_height, original_image_width,_ = original_image.shape\n",
        "\n",
        "    # Grab colormap from dataset for the given number of segmentation classes\n",
        "    # (uses black for the IGNORE class)\n",
        "    # ``colormap`` has shape (num_classes + 1, 3)\n",
        "\n",
        "    # Color per-pixel predictions using the generated color map\n",
        "    # ``colored_predictions_numpy`` has shape (H, W, 3)\n",
        "    predictions_numpy = predictions.cpu().numpy().astype('uint8')\n",
        "    colored_predictions_numpy = colormap[predictions_numpy.flatten()]\n",
        "    colored_predictions_numpy = colored_predictions_numpy.reshape(original_image_height, original_image_width, 3)\n",
        "\n",
        "    # Overlay images and predictions\n",
        "    overlaid_predictions = original_image * fade_weight + colored_predictions_numpy * (1 - fade_weight)\n",
        "\n",
        "    visualized_image = overlaid_predictions.astype('uint8')\n",
        "    return visualized_image"
      ],
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWc1Ug-sSVUP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cm_instance = np.array([[0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [0,  0, 0],\n",
        "                        [220,  20,  60],\n",
        "                        [255,   0,   0],\n",
        "                        [  0,   0, 142],\n",
        "                        [  0,   0,  70],\n",
        "                        [  0,  60, 100],\n",
        "                        [  0,  80, 100],\n",
        "                        [  0,   0, 230],\n",
        "                        [119,  11,  32],\n",
        "                        [  0,   0,   0]])\n",
        "cm_seg = np.array([[128,  64, 128],\n",
        "                  [244,  35, 232],\n",
        "                  [ 70,  70,  70],\n",
        "                  [102, 102, 156],\n",
        "                  [190, 153, 153],\n",
        "                  [153, 153, 153],\n",
        "                  [250, 170,  30],\n",
        "                  [220, 220,   0],\n",
        "                  [107, 142,  35],\n",
        "                  [152, 251, 152],\n",
        "                  [ 70, 130, 180],\n",
        "                  [0,  0, 0],\n",
        "                  [0,  0, 0],\n",
        "                  [0,  0, 0],\n",
        "                  [0,  0, 0],\n",
        "                  [0,  0, 0],\n",
        "                  [0,  0, 0],\n",
        "                  [0,  0, 0],\n",
        "                  [0,  0, 0],\n",
        "                  [0, 0, 0]])\n",
        "LABEL_NAMES = np.asarray([\n",
        "    'road', 'sidewalk', 'building', 'wall', 'fence', 'pole', 'traffic light',\n",
        "    'traffic sign', 'vegetation', 'terrain', 'sky', 'person', 'rider', 'car', 'truck',\n",
        "    'bus', 'train', 'motorcycle', 'bicycle', 'void'])\n",
        "cityscapes_instance_label_name = ['person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle']\n",
        "y_instance = seg_image(semseg_prob[0], input_image, cm_instance, 0)\n",
        "y_seg = seg_image(semseg_prob[0], input_image, cm_seg, 0.5)\n",
        "x = masked_image(instance_detection[0], input_image, cityscapes_instance_label_name)"
      ],
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KgFjg9TlSZC4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "14e3c060-fe6d-4f2b-de5a-3995372c9b29"
      },
      "source": [
        "plt.imshow(y_instance)"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6a2c249588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArRN9ZlvTznc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "e9b677b7-a617-408c-dfad-10871bb374a2"
      },
      "source": [
        "plt.imshow(y_seg)"
      ],
      "execution_count": 207,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6a2c1b6f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 207
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-imCeZytSmNA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "86a8cf5f-f6d8-40a8-c838-a0fd828996cb"
      },
      "source": [
        "plt.imshow(x[0], cmap='gray')"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6a2c198c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qT0unzK-P0ed",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "9ffc5636-28db-46b5-8aed-7eaa294cb27b"
      },
      "source": [
        "def sigmoid(x):\n",
        "  z = 1/(1 + np.exp(x)) \n",
        "  return z\n",
        "s = (sigmoid(x[0]) + sigmoid(y_instance))*(2*x[0])\n",
        "s = s.astype('uint8')"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdhGDkg2P0cf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "d7e03b96-6664-4415-fd03-15cf91b4a981"
      },
      "source": [
        "plt.imshow(s, cmap='gray')"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6a2c0ff780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMWet_3x5Wlc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Range for lower red\n",
        "lower_red = np.array([55, 55, 55])\n",
        "upper_red = np.array([255, 255, 255])\n",
        "mask1 = cv2.inRange(x[1]*255, lower_red, upper_red)\n",
        "mask1 = cv2.bitwise_not(mask1)"
      ],
      "execution_count": 216,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfTVFOSM5eRT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "d96fc072-107b-480a-b401-b6d56fce9f3f"
      },
      "source": [
        "plt.imshow(mask1*255, cmap='gray')"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6a2bf98550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4O7GpUkCe9bG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "9dd85878-7e09-4ab8-87f2-cb1ae0a69d7e"
      },
      "source": [
        "pan = (s + y_seg)*twotothree(mask1*255) + x[1]*255\n",
        "Image.fromarray(pan.astype('uint8')).save('panoptic_segmentation_result.jpg')\n",
        "plt.imshow(pan)"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6a2bc394e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bV-JxIvTP0aR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "visualized_image = np.zeros(x.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUV4K09PP0XO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNbKJUL1zOrQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a2d039ed-a4cf-49ba-e6d4-ee6733bcacf5"
      },
      "source": [
        "x = masked_image(instance_detection[0], input_image, cityscapes_instance_label_name, y_instance)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BoxList(num_boxes=66, image_width=2048, image_height=1024, mode=xyxy)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gaGIUhyEqpUV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def panoptic(input_image):\n",
        "  # Prepare for model inference.\n",
        "  model.eval()\n",
        "  data = {'image': input_image}\n",
        "  # data pre-processing\n",
        "  normalize_transform = P.Normalize(mean=cfg.input.pixel_mean, std=cfg.input.pixel_std, to_bgr255=cfg.input.to_bgr255)\n",
        "  transform = P.Compose([\n",
        "      P.ToTensor(),\n",
        "      normalize_transform,\n",
        "  ])\n",
        "  data = transform(data)\n",
        "  #print(\"Done with data preparation and model configuration.\")\n",
        "  with torch.no_grad():\n",
        "      input_image_list = ImageList([data['image'].to(device)], image_sizes=[input_image.size[::-1]])\n",
        "      panoptic_result, _ = model.forward(input_image_list)\n",
        "      #print(\"Done with model inference.\")\n",
        "      #print(\"Process and visualizing the outputs...\")\n",
        "      instance_detection = [o.to('cpu') for o in panoptic_result[\"instance_segmentation_result\"]]\n",
        "      semseg_logics = [o.to('cpu') for o in panoptic_result[\"semantic_segmentation_result\"]]\n",
        "      semseg_prob = [torch.argmax(semantic_logit , dim=0) for semantic_logit in  semseg_logics]\n",
        "      seg_vis = visualize_segmentation_image(semseg_prob[0], input_image, cityscapes_colormap)\n",
        "      #print(\"Saved semantic segmentation visualization in semantic_segmentation_result.jpg\")\n",
        "      det_vis = visualize_detection_image(instance_detection[0], input_image, cityscapes_instance_label_name)\n",
        "      #Image.fromarray(det_vis.astype('uint8')).save('instance_segmentation_result.jpg')\n",
        "      #print(\"Saved instance segmentation visualization in instance_segmentation_result.jpg\")\n",
        "      #print(\"Demo finished.\")\n",
        "      return seg_vis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSU75H_ycXWp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "from PIL import Image\n",
        "frame = cv2.imread('/content/realtime_panoptic/media/figs/test.png')\n",
        "frame = Image.fromarray(np.uint8(frame)).convert('RGB')\n",
        "z = panoptic(frame) \n",
        "Image.fromarray(z.astype('uint8')).save(\"/content/{}.jpg\".format(1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-mMJ8309BUC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2bf64b01-9a73-4db4-b76b-135a9feed0b6"
      },
      "source": [
        "!wget http://s150102174.onlinehome.fr/Lara/files/Lara_UrbanSeq1_MPEG2.mpg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-05 21:08:54--  http://s150102174.onlinehome.fr/Lara/files/Lara_UrbanSeq1_MPEG2.mpg\n",
            "Resolving s150102174.onlinehome.fr (s150102174.onlinehome.fr)... 217.160.0.161\n",
            "Connecting to s150102174.onlinehome.fr (s150102174.onlinehome.fr)|217.160.0.161|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 208220164 (199M) [video/mpeg]\n",
            "Saving to: ‘Lara_UrbanSeq1_MPEG2.mpg’\n",
            "\n",
            "Lara_UrbanSeq1_MPEG 100%[===================>] 198.57M  6.92MB/s    in 29s     \n",
            "\n",
            "2020-08-05 21:09:24 (6.78 MB/s) - ‘Lara_UrbanSeq1_MPEG2.mpg’ saved [208220164/208220164]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-h9R-1iSfDe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "5f0999d8-2986-4708-ae17-1a17f2294338"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDcv8FtJ-Zsq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2 \n",
        "from PIL import Image\n",
        "import time\n",
        "\n",
        "video = cv2.VideoCapture('/content/Lara_UrbanSeq1_MPEG2.mpg')\n",
        "width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
        "height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
        "\n",
        "# We need to check if camera \n",
        "# is opened previously or not \n",
        "if (video.isOpened() == False): \n",
        "\tprint(\"Error reading video file\") \n",
        "\n",
        "# We need to set resolutions. \n",
        "size = (width, height) \n",
        "\n",
        "count = 1\n",
        "# Below VideoWriter object will create \n",
        "# a frame of above defined The output \n",
        "# is stored in 'filename.avi' file. \n",
        "start_time = time.time()\n",
        "while(True): \n",
        "  ret, frame = video.read()\n",
        "  print(count)\n",
        "  if ret == True:\n",
        "    frame = Image.fromarray(np.uint8(frame)).convert('RGB')\n",
        "\n",
        "    z = panoptic(frame) \n",
        "    Image.fromarray(z.astype('uint8')).save(\"/content/drive/My Drive/DrivingImages/{}.jpg\".format(count))\n",
        "      \n",
        "    count = count + 1\n",
        "    #cv2_imshow(detr(frame))\n",
        "    # Break the loop \n",
        "\n",
        "  else:\n",
        "    break\n",
        "\n",
        "# When everything done, release \n",
        "# the video capture and video \n",
        "# write objects \n",
        "video.release() \n",
        "\n",
        "# Closes all the frames \n",
        "cv2.destroyAllWindows() \n",
        "\n",
        "print(\"The video was successfully saved\") \n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))\n",
        "t = time.time() - start_time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsdgGwx-mh4p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f73514bf-e544-438f-c06b-38b654c6c17a"
      },
      "source": [
        "11169/t"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.115676812987131"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ad0Op647RxY7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "91df743b-9cbf-4c4c-caf4-095f1cd10153"
      },
      "source": [
        "import os\n",
        "import moviepy.video.io.ImageSequenceClip\n",
        "image_folder='/content/drive/My Drive/DrivingImages'\n",
        "fps=30\n",
        "\n",
        "image_files = [image_folder+'/'+img for img in os.listdir(image_folder) if img.endswith(\".jpg\")]\n",
        "clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)\n",
        "clip.write_videofile('/content/drive/My Drive/myVideo.mp4')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[MoviePy] >>>> Building video /content/drive/My Drive/myVideo.mp4\n",
            "[MoviePy] Writing video /content/drive/My Drive/myVideo.mp4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 11169/11169 [05:14<00:00, 35.51it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: /content/drive/My Drive/myVideo.mp4 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kjnw4Sangrj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "01f44057-228d-4d49-e9c7-be6b911ae892"
      },
      "source": [
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RTPanoNet(\n",
              "  (backbone): ResNetWithModifiedFPN(\n",
              "    (body): IntermediateLayerGetter(\n",
              "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "      (bn1): FrozenBatchNorm2d()\n",
              "      (relu): ReLU(inplace)\n",
              "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "      (layer1): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (1): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "      )\n",
              "      (layer2): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "      )\n",
              "      (layer3): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (3): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (4): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (5): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "      )\n",
              "      (layer4): Sequential(\n",
              "        (0): Bottleneck(\n",
              "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "          (downsample): Sequential(\n",
              "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "            (1): FrozenBatchNorm2d()\n",
              "          )\n",
              "        )\n",
              "        (1): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "        (2): Bottleneck(\n",
              "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn1): FrozenBatchNorm2d()\n",
              "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "          (bn2): FrozenBatchNorm2d()\n",
              "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn3): FrozenBatchNorm2d()\n",
              "          (relu): ReLU(inplace)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (fpn): FeaturePyramidNetwork(\n",
              "      (inner_blocks): ModuleList(\n",
              "        (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (2): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "      (layer_blocks): ModuleList(\n",
              "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      )\n",
              "      (extra_blocks): LastLevelP6P7(\n",
              "        (p6): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        (p7): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (panoptic_head): PanopticHead(\n",
              "    (cls_tower): Sequential(\n",
              "      (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (2): ReLU()\n",
              "      (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (5): ReLU()\n",
              "      (6): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (7): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (8): ReLU()\n",
              "      (9): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (10): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
              "      (11): ReLU()\n",
              "    )\n",
              "    (bbox_tower): Sequential(\n",
              "      (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (2): ReLU()\n",
              "      (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (4): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (5): ReLU()\n",
              "      (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (7): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (8): ReLU()\n",
              "      (9): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (10): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
              "      (11): ReLU()\n",
              "    )\n",
              "    (cls_logits): Conv2d(640, 19, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (box_cls_logits): Conv2d(128, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (bbox_pred): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (centerness): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (levelness): Conv2d(1280, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (scales): ModuleList(\n",
              "      (0): Scale()\n",
              "      (1): Scale()\n",
              "      (2): Scale()\n",
              "      (3): Scale()\n",
              "      (4): Scale()\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tkuI1iss9tH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pytorch_total_params = sum(p.numel() for p in model.parameters())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7pcAwb2tQQj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "34333c2f-b7e7-471e-a6fb-e80dbe47c11a"
      },
      "source": [
        "pytorch_total_params"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30624107"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiLM00vXtR9E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "432ec32d-69c8-4696-9974-571b5fcda055"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mP4YDTngxuZt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}